Technical Field
The present invention relates to data processing and more particularly to large margin high-order deep learning with auxiliary tasks for video-based anomaly detection.
Description of the Related Art
Recently, there has been increasing interest within computer vision in analyzing anomalous events based on image collections or video sequences. Such analysis has been directed to tasks such as, for example, product inspection for defects in factories, autonomous driving, public safety video surveillance, and so forth.
One approach developed to perform these tasks is based on trajectory modeling. In trajectory modeling, the main idea is to track each object in the video sequences and learn models for the object tracks. These tasks, however, are difficult for video sequences which include dense objects. Another approach is based upon motion representations such as dense optical flow, or spatio-temporal gradients.
These existing approaches focus on modeling motion information while ignoring the high-order feature interaction representation within each static image and may not perform well for supervised learning since only limited number of positive examples/labels are available. High-order feature interactions naturally exist in many real-world data, including images, video sequences, financial time series, biomedical informatics data, and so forth. These interplays often convey essential information about the latent structures of the data. For anomaly detection, therefore, it is crucial to consider these high-order characteristic features while making the decision.